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        Package&nbsp;deepbelief
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<h1 class="epydoc">Source Code for <a href="deepbelief-module.html">Package deepbelief</a></h1>
<pre class="py-src">
<a name="L1"></a><tt class="py-lineno"> 1</tt>  <tt class="py-line"><tt class="py-docstring">"""</tt> </tt>
<a name="L2"></a><tt class="py-lineno"> 2</tt>  <tt class="py-line"><tt class="py-docstring">Introduction</tt> </tt>
<a name="L3"></a><tt class="py-lineno"> 3</tt>  <tt class="py-line"><tt class="py-docstring">============</tt> </tt>
<a name="L4"></a><tt class="py-lineno"> 4</tt>  <tt class="py-line"><tt class="py-docstring">        This package implements algorithms for training and evaluating deep belief</tt> </tt>
<a name="L5"></a><tt class="py-lineno"> 5</tt>  <tt class="py-line"><tt class="py-docstring">        networks (DBNs). Currently, the following variants of the restricted Boltzmann</tt> </tt>
<a name="L6"></a><tt class="py-lineno"> 6</tt>  <tt class="py-line"><tt class="py-docstring">        machine are available for constructing DBNs:</tt> </tt>
<a name="L7"></a><tt class="py-lineno"> 7</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L8"></a><tt class="py-lineno"> 8</tt>  <tt class="py-line"><tt class="py-docstring">                - L{RBM}</tt> </tt>
<a name="L9"></a><tt class="py-lineno"> 9</tt>  <tt class="py-line"><tt class="py-docstring">                - L{GaussianRBM}</tt> </tt>
<a name="L10"></a><tt class="py-lineno">10</tt>  <tt class="py-line"><tt class="py-docstring">                - L{SemiRBM}</tt> </tt>
<a name="L11"></a><tt class="py-lineno">11</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L12"></a><tt class="py-lineno">12</tt>  <tt class="py-line"><tt class="py-docstring">        Also have a look a L{AbstractBM} which specifies much of the interface and</tt> </tt>
<a name="L13"></a><tt class="py-lineno">13</tt>  <tt class="py-line"><tt class="py-docstring">        learning algorithms. In order to evaluate a trained DBN, the L{Estimator}</tt> </tt>
<a name="L14"></a><tt class="py-lineno">14</tt>  <tt class="py-line"><tt class="py-docstring">        class can be used to estimate the the likelihood of the trained model.</tt> </tt>
<a name="L15"></a><tt class="py-lineno">15</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L16"></a><tt class="py-lineno">16</tt>  <tt class="py-line"><tt class="py-docstring">Miscellaneous</tt> </tt>
<a name="L17"></a><tt class="py-lineno">17</tt>  <tt class="py-line"><tt class="py-docstring">=============</tt> </tt>
<a name="L18"></a><tt class="py-lineno">18</tt>  <tt class="py-line"><tt class="py-docstring">        For questions, comments or bug reports, contact U{Lucas Theis&lt;mailto:lucas@tuebingen.mpg.de&gt;}.</tt> </tt>
<a name="L19"></a><tt class="py-lineno">19</tt>  <tt class="py-line"><tt class="py-docstring">        This code is published under the U{MIT License&lt;http://www.opensource.org/licenses/mit-license.php&gt;}.</tt> </tt>
<a name="L20"></a><tt class="py-lineno">20</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L21"></a><tt class="py-lineno">21</tt>  <tt class="py-line"><tt class="py-docstring">Quick Tutorial</tt> </tt>
<a name="L22"></a><tt class="py-lineno">22</tt>  <tt class="py-line"><tt class="py-docstring">==============</tt> </tt>
<a name="L23"></a><tt class="py-lineno">23</tt>  <tt class="py-line"><tt class="py-docstring">        Assume that C{data} is a 10 by 1000 numpy matrix with real entries containing</tt> </tt>
<a name="L24"></a><tt class="py-lineno">24</tt>  <tt class="py-line"><tt class="py-docstring">        1000 data points.  We want a deep belief network to learn the distribution of</tt> </tt>
<a name="L25"></a><tt class="py-lineno">25</tt>  <tt class="py-line"><tt class="py-docstring">        the data.</tt> </tt>
<a name="L26"></a><tt class="py-lineno">26</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L27"></a><tt class="py-lineno">27</tt>  <tt class="py-line"><tt class="py-docstring">                &gt;&gt;&gt; print data.shape</tt> </tt>
<a name="L28"></a><tt class="py-lineno">28</tt>  <tt class="py-line"><tt class="py-docstring">                (10, 1000)</tt> </tt>
<a name="L29"></a><tt class="py-lineno">29</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L30"></a><tt class="py-lineno">30</tt>  <tt class="py-line"><tt class="py-docstring">        Import the relevant building blocks</tt> </tt>
<a name="L31"></a><tt class="py-lineno">31</tt>  <tt class="py-line"><tt class="py-docstring">        for constructing deep belief networks.</tt> </tt>
<a name="L32"></a><tt class="py-lineno">32</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L33"></a><tt class="py-lineno">33</tt>  <tt class="py-line"><tt class="py-docstring">                &gt;&gt;&gt; from deepbelief import RBM, GaussianRBM, DBN</tt> </tt>
<a name="L34"></a><tt class="py-lineno">34</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L35"></a><tt class="py-lineno">35</tt>  <tt class="py-line"><tt class="py-docstring">        Create a first layer with 10 visible units and 50 hidden units. The</tt> </tt>
<a name="L36"></a><tt class="py-lineno">36</tt>  <tt class="py-line"><tt class="py-docstring">        L{GaussianRBM} is suited for modeling continuous data.</tt> </tt>
<a name="L37"></a><tt class="py-lineno">37</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L38"></a><tt class="py-lineno">38</tt>  <tt class="py-line"><tt class="py-docstring">                &gt;&gt;&gt; dbn = DBN(GaussianRBM(10, 50))</tt> </tt>
<a name="L39"></a><tt class="py-lineno">39</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L40"></a><tt class="py-lineno">40</tt>  <tt class="py-line"><tt class="py-docstring">        Split the data into batches of size 10 and train the first layer for 50</tt> </tt>
<a name="L41"></a><tt class="py-lineno">41</tt>  <tt class="py-line"><tt class="py-docstring">        iterations.</tt> </tt>
<a name="L42"></a><tt class="py-lineno">42</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L43"></a><tt class="py-lineno">43</tt>  <tt class="py-line"><tt class="py-docstring">                &gt;&gt;&gt; dbn.train(data, num_epochs=50, batch_size=10)</tt> </tt>
<a name="L44"></a><tt class="py-lineno">44</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L45"></a><tt class="py-lineno">45</tt>  <tt class="py-line"><tt class="py-docstring">        Add a second layer to the network.</tt> </tt>
<a name="L46"></a><tt class="py-lineno">46</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L47"></a><tt class="py-lineno">47</tt>  <tt class="py-line"><tt class="py-docstring">                &gt;&gt;&gt; dbn.add_layer(RBM(50, 50))</tt> </tt>
<a name="L48"></a><tt class="py-lineno">48</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L49"></a><tt class="py-lineno">49</tt>  <tt class="py-line"><tt class="py-docstring">        Train the second layer.</tt> </tt>
<a name="L50"></a><tt class="py-lineno">50</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L51"></a><tt class="py-lineno">51</tt>  <tt class="py-line"><tt class="py-docstring">                &gt;&gt;&gt; dbn.train(data, num_epochs=50, batch_size=10)</tt> </tt>
<a name="L52"></a><tt class="py-lineno">52</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L53"></a><tt class="py-lineno">53</tt>  <tt class="py-line"><tt class="py-docstring">        Generate another 500 data points by sampling from the trained model.</tt> </tt>
<a name="L54"></a><tt class="py-lineno">54</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L55"></a><tt class="py-lineno">55</tt>  <tt class="py-line"><tt class="py-docstring">                &gt;&gt;&gt; samples = dbn.sample(500)</tt> </tt>
<a name="L56"></a><tt class="py-lineno">56</tt>  <tt class="py-line"><tt class="py-docstring">"""</tt> </tt>
<a name="L57"></a><tt class="py-lineno">57</tt>  <tt class="py-line"><tt class="py-keyword">from</tt> <tt id="link-0" class="py-name" targets="Module deepbelief.dbn=deepbelief.dbn-module.html"><a title="deepbelief.dbn" class="py-name" href="#" onclick="return doclink('link-0', 'dbn', 'link-0');">dbn</a></tt> <tt class="py-keyword">import</tt> <tt id="link-1" class="py-name" targets="Class deepbelief.dbn.DBN=deepbelief.dbn.DBN-class.html"><a title="deepbelief.dbn.DBN" class="py-name" href="#" onclick="return doclink('link-1', 'DBN', 'link-1');">DBN</a></tt> </tt>
<a name="L58"></a><tt class="py-lineno">58</tt>  <tt class="py-line"><tt class="py-keyword">from</tt> <tt id="link-2" class="py-name" targets="Module deepbelief.rbm=deepbelief.rbm-module.html"><a title="deepbelief.rbm" class="py-name" href="#" onclick="return doclink('link-2', 'rbm', 'link-2');">rbm</a></tt> <tt class="py-keyword">import</tt> <tt id="link-3" class="py-name" targets="Class deepbelief.rbm.RBM=deepbelief.rbm.RBM-class.html"><a title="deepbelief.rbm.RBM" class="py-name" href="#" onclick="return doclink('link-3', 'RBM', 'link-3');">RBM</a></tt> </tt>
<a name="L59"></a><tt class="py-lineno">59</tt>  <tt class="py-line"><tt class="py-keyword">from</tt> <tt id="link-4" class="py-name" targets="Module deepbelief.gaussianrbm=deepbelief.gaussianrbm-module.html"><a title="deepbelief.gaussianrbm" class="py-name" href="#" onclick="return doclink('link-4', 'gaussianrbm', 'link-4');">gaussianrbm</a></tt> <tt class="py-keyword">import</tt> <tt id="link-5" class="py-name" targets="Class deepbelief.gaussianrbm.GaussianRBM=deepbelief.gaussianrbm.GaussianRBM-class.html"><a title="deepbelief.gaussianrbm.GaussianRBM" class="py-name" href="#" onclick="return doclink('link-5', 'GaussianRBM', 'link-5');">GaussianRBM</a></tt> </tt>
<a name="L60"></a><tt class="py-lineno">60</tt>  <tt class="py-line"><tt class="py-keyword">from</tt> <tt id="link-6" class="py-name" targets="Module deepbelief.semirbm=deepbelief.semirbm-module.html"><a title="deepbelief.semirbm" class="py-name" href="#" onclick="return doclink('link-6', 'semirbm', 'link-6');">semirbm</a></tt> <tt class="py-keyword">import</tt> <tt id="link-7" class="py-name" targets="Class deepbelief.semirbm.SemiRBM=deepbelief.semirbm.SemiRBM-class.html"><a title="deepbelief.semirbm.SemiRBM" class="py-name" href="#" onclick="return doclink('link-7', 'SemiRBM', 'link-7');">SemiRBM</a></tt> </tt>
<a name="L61"></a><tt class="py-lineno">61</tt>  <tt class="py-line"><tt class="py-keyword">from</tt> <tt id="link-8" class="py-name" targets="Module deepbelief.estimator=deepbelief.estimator-module.html"><a title="deepbelief.estimator" class="py-name" href="#" onclick="return doclink('link-8', 'estimator', 'link-8');">estimator</a></tt> <tt class="py-keyword">import</tt> <tt id="link-9" class="py-name" targets="Class deepbelief.estimator.Estimator=deepbelief.estimator.Estimator-class.html"><a title="deepbelief.estimator.Estimator" class="py-name" href="#" onclick="return doclink('link-9', 'Estimator', 'link-9');">Estimator</a></tt> </tt>
<a name="L62"></a><tt class="py-lineno">62</tt>  <tt class="py-line"><tt class="py-keyword">from</tt> <tt id="link-10" class="py-name" targets="Module deepbelief.mixbm=deepbelief.mixbm-module.html"><a title="deepbelief.mixbm" class="py-name" href="#" onclick="return doclink('link-10', 'mixbm', 'link-10');">mixbm</a></tt> <tt class="py-keyword">import</tt> <tt id="link-11" class="py-name" targets="Class deepbelief.mixbm.MixBM=deepbelief.mixbm.MixBM-class.html"><a title="deepbelief.mixbm.MixBM" class="py-name" href="#" onclick="return doclink('link-11', 'MixBM', 'link-11');">MixBM</a></tt> </tt>
<a name="L63"></a><tt class="py-lineno">63</tt>  <tt class="py-line"><tt class="py-keyword">from</tt> <tt id="link-12" class="py-name" targets="Module deepbelief.abstractbm=deepbelief.abstractbm-module.html"><a title="deepbelief.abstractbm" class="py-name" href="#" onclick="return doclink('link-12', 'abstractbm', 'link-12');">abstractbm</a></tt> <tt class="py-keyword">import</tt> <tt id="link-13" class="py-name" targets="Class deepbelief.abstractbm.AbstractBM=deepbelief.abstractbm.AbstractBM-class.html"><a title="deepbelief.abstractbm.AbstractBM" class="py-name" href="#" onclick="return doclink('link-13', 'AbstractBM', 'link-13');">AbstractBM</a></tt> </tt>
<a name="L64"></a><tt class="py-lineno">64</tt>  <tt class="py-line"> </tt>
<a name="L65"></a><tt class="py-lineno">65</tt>  <tt class="py-line"><tt class="py-name">__docformat__</tt> <tt class="py-op">=</tt> <tt class="py-string">'epytext'</tt> </tt>
<a name="L66"></a><tt class="py-lineno">66</tt>  <tt class="py-line"> </tt><script type="text/javascript">
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